Xingxing Gao

Title: Innovations of Xingxing Gao in Photovoltaic Power Output Prediction

Introduction

Xingxing Gao is an accomplished inventor based in Liaoning, China. He has made significant contributions to the field of renewable energy, particularly in the prediction of photovoltaic power output. His innovative approach combines advanced algorithms with machine learning techniques to enhance the accuracy of power output predictions.

Latest Patents

Xingxing Gao holds a patent for a "Short-term interval prediction method for photovoltaic power output." This method utilizes a combination of a multi-objective optimization algorithm and a least square support vector machine. The invention introduces a similar day classification method that enhances the regularity of samples by considering both numerical value and pattern similarity. Additionally, it constructs an adaptive proportional interval estimation model based on a dual-LSSVM model, optimizing model parameters using NSGA-II algorithms. The results demonstrate high accuracy and computational efficiency, making it suitable for practical applications in grid connection and scheduling of renewable energy sources.

Career Highlights

Xingxing Gao is affiliated with Dalian University of Technology, where he continues to engage in research and development in the field of renewable energy. His work has garnered attention for its innovative methodologies and practical applications.

Collaborations

Xingxing Gao has collaborated with notable colleagues, including Feng Jin and Jun Zhao, contributing to advancements in their shared field of expertise.

Conclusion

Xingxing Gao's contributions to the prediction of photovoltaic power output exemplify the potential of innovative methodologies in renewable energy. His work not only enhances prediction accuracy but also supports the broader application of renewable energy technologies.